Aerodynamic Optimization Design of a 150 kW High Performance Supercritical Carbon Dioxide Centrifugal Compressor without a High Speed Requirement

Shi, Dongbo; Xie, Yonghui

doi:10.3390/app10062093

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…Then, further 3-D aerodynamic optimization was carried out, mainly including inlet and outlet flow angle correction, flow matching of rotor and stator blades, blade profile optimization, etc. These optimization methods have achieved good results in previous research [24,44,45]. The detailed thermodynamic design parameters, geometric parameters, performance parameters, and blade profiles of the designed S-CO 2 turbine are shown in Table 2.…”

Section: Cfd Off-design Pre-analysismentioning

confidence: 85%

“…In previous research, the S-CO 2 compressor with more complex flow was used for numerical verification. By comparing with the numerical and experimental results of other scholars, it can be shown that the numerical method is accurate [45]. For the off-design performance analysis of the turbine, the turbine's inlet temperature, inlet pressure, inlet airflow angle, mass flow rate, and rotating speed were changed change within ±15% of the design value.…”

Section: Cfd Off-design Pre-analysismentioning

confidence: 92%

“…The final number of selected grid nodes was 420,000. The numerical method was the same as previous research [24,[43][44][45]. In previous research, the S-CO 2 compressor with more complex flow was used for numerical verification.…”

Section: Cfd Off-design Pre-analysismentioning

confidence: 99%

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Off-Design Performance Prediction of a S-CO2 Turbine Based on Field Reconstruction Using Deep-Learning Approach

2020

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The reliable design of the supercritical carbon dioxide (S-CO2) turbine is the core of the advanced S-CO2 power generation technology. However, the traditional computational fluid dynamics (CFD) method is usually applied in the S-CO2 turbine design-optimization, which is a high computational cost, high memory requirement, and long time-consuming solver. In this research, a flexible end-to-end deep learning approach is presented for the off-design performance prediction of the S-CO2 turbine based on physical fields reconstruction. Our approach consists of three steps: firstly, an optimal design of a 60,000 rpm S-CO2 turbine is established. Secondly, five design variables for off-design analysis are selected to reconstruct the temperature and pressure fields on the blade surface through a deconvolutional neural network. Finally, the power and efficiency of the turbine is predicted by a convolutional neural network according to reconstruction fields. The results show that the prediction approach not only outperforms five classical machine learning models but also focused on the physical mechanism of turbine design. In addition, once the deep model is well-trained, the calculation with graphics processing unit (GPU)-accelerated can quickly predict the physical fields and performance. This prediction approach requires less human intervention and has the advantages of being universal, flexible, and easy to implement.

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Section: Cfd Off-design Pre-analysismentioning

confidence: 85%

Section: Cfd Off-design Pre-analysismentioning

confidence: 92%

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Off-Design Performance Prediction of a S-CO2 Turbine Based on Field Reconstruction Using Deep-Learning Approach

2020

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“…The airflow enters the inlet chamber from the radial direction and turns into the axial direction. This process will produce a certain flow loss and outlet distortion, which will lead to the deterioration of the performance of downstream components, 4,5 and thus directly affect the overall efficiency of CAES system and the economy of energy storage. Therefore, it is necessary to optimize the structural shape of the RIC to reduce the internal flow loss and make the outlet flow more uniform, thereby improving the performance of the compressor and efficiency of CAES system.…”

Section: Introductionmentioning

confidence: 99%

Genetic optimization design for aerodynamic structure of oblique flow compressor radial inlet chamber in compressed air energy storage system

et al. 2023

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The compressor plays a significant role in the compressed air energy storage (CAES) system, and its performance directly determines the overall efficiency of the system and the economy of energy storage. Internal flow losses and outlet distortion due to poor design of its radial inlet chamber (RIC) geometry can degrade the performance of downstream components and reduce compressor unit performance. In this article, a multiobjective optimization design is carried out for the RIC of the oblique flow compressor in CAES system, and a parametric optimization design method of the RIC annular convergent channel meridian profile is developed. The optimal Latin hypercube experimental design (opt LHD) method is used to generate the sample library, and the radial basis function neural network (RBFNN) is used to establish the surrogate model of each design parameter and RIC performance. The multiobjective optimization is executed by the second‐generation non‐dominated sorting genetic algorithm (NSGA‐II). After optimization, the area of the annular convergent channel of the RIC is expanded, the transition of the meridian profile is smoother, the distortion coefficient of the RIC is reduced by 0.58%, and the total pressure loss coefficient is reduced by 16.45%. By using the optimized RIC, the performance of oblique flow compressor has been improved. Compared with the original model, the total pressure ratio and isentropic efficiency of the whole machine at the design point have been increased by 0.6% and 0.32%, respectively.

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“…The devices that transport working fluids from the pipelines or the atmosphere to the impeller inlet are called inlet chambers, and radial inlet chambers are the key upstream component widely used in large-scale multistage centrifugal compressors due to the layout constraints of compact industrial systems [3,4]. However, compared with axial inlet ducts, the radial inlet chamber will introduce extra flow loss and distortions, which negatively influence the compressor's performance [5,6]. Therefore, it requires an improved design to avoid flow separations in the chamber, reduce flow loss and achieve uniform flow distribution at the outlet of the radial inlet chamber [7,8].…”

Section: Introductionmentioning

confidence: 99%

Flow Control of Radial Inlet Chamber and Downstream Effects on a Centrifugal Compressor Stage

et al. 2021

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Radial inlet chambers are widely used in various multistage centrifugal compressors, although they induce extra flow loss and inlet distortions. In this paper, the detailed flow characteristics inside the radial inlet chamber of an industrial centrifugal compressor have been numerically investigated for flow control and performance improvement. First, the numerical results are validated against the experimental data, and flow conditions inside the inlet chambers with different structures are compared. They indicate that, in the non-guide vane scheme, sudden expansions, tangential flows and flow separations in the spiral and annular convergent channels are the major causes of flow loss and distortions, while using guide vanes could introduce additional flow impacts, separations and wakes. Based on the flow analysis, structure improvements have been carried out on the radial inlet chamber, and an average increase of 4.97% has been achieved in the inlet chamber efficiencies over different operating conditions. However, the results further reveal that the increases in the performance and overall flow uniformity just in the radial inlet chamber do not necessarily mean a performance improvement in the downstream components, and the distribution of the positive tangential velocity at the impeller inlet might be a more essential factor for the efficiency of the whole compressor.

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Aerodynamic Optimization Design of a 150 kW High Performance Supercritical Carbon Dioxide Centrifugal Compressor without a High Speed Requirement

Cited by 9 publications

References 22 publications

Off-Design Performance Prediction of a S-CO2 Turbine Based on Field Reconstruction Using Deep-Learning Approach

Off-Design Performance Prediction of a S-CO2 Turbine Based on Field Reconstruction Using Deep-Learning Approach

Genetic optimization design for aerodynamic structure of oblique flow compressor radial inlet chamber in compressed air energy storage system

Flow Control of Radial Inlet Chamber and Downstream Effects on a Centrifugal Compressor Stage

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